AWS GPU Instances → RunPod
What it saves, what actually moves, what you rebuild — and the thing that catches people.
What it costs, and what it saves
H100 capacity is about $12.29 per GPU-hour on-demand at AWS against roughly $1.99 at RunPod — a five-to-six-fold difference for identical silicon. A single H100 running 8 hours a day drops from about $2,950 a month to $480.
Moves cleanly
The container. If your training or inference runs in Docker with CUDA, it runs on RunPod essentially unchanged.
You rebuild
Anything wired to AWS services around the instance — S3 paths, IAM roles, SageMaker orchestration, VPC assumptions.
What AWS GPU Instances costs you today
On-demand H100 capacity on AWS runs around $12.29 per GPU-hour, against roughly $1.99 at RunPod and $1.87 on the Vast.ai marketplace — a five-to-six-fold difference for the same silicon. Savings Plans and Reserved Instances cut 30–50% in exchange for commitment, and Spot capacity cuts more in exchange for interruption. Data egress is billed separately and is the line most people forget when they model a move. Prices checked 2026-07-30; GPU pricing moves faster than almost anything else in cloud, so confirm current rates before you plan around them.
What actually holds you in
Lower than most AWS services, which is the good news. A container running CUDA is portable, so the model code usually moves unchanged. What holds you are the things around it: training data sitting in S3 with egress charges to move it, IAM and VPC wiring, and any dependency on SageMaker, Bedrock or EKS orchestration built around the instances rather than inside them.
What you are moving to
RunPod rents GPUs by the second across a global fleet, with two useful modes: persistent Pods for interactive work, and Serverless for inference that scales to zero between requests so an idle endpoint costs nothing. H100 capacity sits around $1.99 per hour against roughly $12.29 on AWS. The developer experience is the real draw — bring a Docker image, pick a GPU, and you are running in well under a minute, with no quota request and no commitment. It has become the default place people go to test whether a model works before deciding where it should live.
Per-second billing, no commitment. H100 around $1.99/hr, with cheaper Community Cloud capacity and a pricier Secure Cloud tier. Serverless scales to zero, so idle inference endpoints cost nothing. Verified 2026-07-30.
RunPod strengths
- Roughly a sixth of AWS on-demand H100 pricing for the same silicon
- Per-second billing with no commitment — start and stop freely
- Serverless scale-to-zero means idle inference endpoints cost nothing
- Standard Docker images, so workloads stay portable to any other provider
What you give up
- Community Cloud runs on partner hardware — reliability varies by host
- Not the venue for workloads needing formal enterprise compliance attestations
- Capacity for the newest GPUs can be tight at peak times
- A hosted service: your data and your model sit on someone else's machine
The migration, step by step
- 1Containerise the workload if it is not already; this is the only real prerequisite
- 2Push the image to a registry RunPod can pull from
- 3Start with a Pod for interactive work, or Serverless if the workload is bursty
- 4Move training data — and price the S3 egress before you start, because it can exceed the first month's saving
- 5Keep AWS capacity until you have run a full training cycle elsewhere
The gotcha
S3 egress. Moving a large training set out of AWS is billed per GB and teams discover the bill after committing. Calculate it first; sometimes the answer is to keep data in S3 and accept slower loads.
When to stay on AWS GPU Instances
You need compliance attestations, a VPC that never touches the public internet, or guaranteed capacity under contract.
Going ahead? Start with RunPodMore Layer 3 migrations
Figures verified 2026-07-30 against vendor pricing pages. Prices change and migrations differ by estate — treat the cost delta as a starting model, not a quote. Rankings and recommendations here are merit-only; affiliate income never changes a verdict. See our methodology.